Monitoring and modelling of the district

A monitoring campaign was designed to investigate the electricity consumption of the buildings of the district at 15 minutes intervals. The monitoring involved all the 25 dwellings as well as the school, during the whole year 2016. In Figure 99 the annual electricity consumption measured for the different households as well as the school is shown. As it can be seen, the differences among dwellings are quite pronounced (for instance, ID 15 consumes 5 times more than ID 18). These differences will allow us to exploit the synergies between buildings with different energy consumptions.

Figure 99: Annual electricity consumption of every building in the district.

6.2.2.2. PV production and electrical balance of the district

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weather, shading, wiring, component efficiencies, panel mismatches and aging. It also provides recommendations for equipment and array layout, calculating the discrete number of PV modules that can fit into a solar field and considering parameters such as the orientation and inclination of the modules or the separation between adjacent rows.

Figure 100: Depiction of the PV modules installed in the district.

For the whole period of the study, weather data was available through a nearby weather station, providing hourly values of outdoor temperature, humidity, and solar radiation. The simulation of the PV potential (using a weather file that contained the real data of the monitoring period, following the procedure in Annex A) allowed to estimate the electricity production of the district, obtaining the results shown in Figure 101. These results are also compared to the monthly real electricity consumption of the district. As is apparent, the amount of electricity that could be produced by the PV modules in the district is higher than the monthly electricity consumption. However, this assertion is rather misleading, since one might think that no electricity would need to be imported. Since there might be times with electricity surplus and times (for example during the night) were electricity would have to be imported once the batteries are depleted, the adjustment between supply and demand at short time scales should be considered.

Figure 101: Monthly PV production of the district.

Adopting a 15 minutes time scale for analyzing the balance between the electricity consumption and the estimated PV production of each building provides interesting insights. Let us take a couple of days in January as an example (see Figure 102), focusing on two specific dwellings: ID 11 and ID 14. In this case, ID 14 is a dwelling occupied by one person who was away on holidays. For that reason, its PV production during the day would allow to charge the batteries, reaching a 100 % State of Charge (SOC) early in the morning. This means that this dwelling would have to sell the electricity on the retail market during this period (or get rid of it if that were not possible). Due to the low electricity consumptions (standby and fridge) during the day, the battery SOC drops only around 20 %, until it is charged again the following day. On the other hand, ID 11 is a dwelling occupied by two people who were present during the holidays. Their high

electricity consumption meant that electricity had to be taken from the batteries even during daylight hours with own PV production, and then from the grid once their battery was depleted.

Figure 102: Electricity surplus of the two dwellings under study, calculated through the difference between PV production and electricity consumption. Positive values mean an electricity surplus, while negative

values mean the need to import electricity.

Figure 102 reveals the importance of the strategies that will be proposed in the present work. In times were the battery of a dwelling is fully charged while at the same time other dwellings are in need of importing electricity, this electricity could be shared between them. This could bring economic benefits, due to the fact that the selling price for exported electricity is lower than that for the imported electricity (or even there might be no Feed-in-Tariff available). Furthermore, even if the battery of a dwelling is not full, it might be more beneficial to share the electricity with a household who needs to import it, since the electricity losses when charging and discharging the batteries would be avoided. Preconditioning strategies as well as a dynamic pricing framework could also help to detect optimal dispatching strategies. The present work aims to answer these questions.

6.2.2.3. Electricity prices

For the purposes of this study, a two-period tariff will be considered. The hourly real prices of the electricity market in Spain in 2016 (coinciding with the monitoring period) for this tariff were taken from a public organism of the Spanish government (https://www.cnmc.es). An example of these variable prices is shown in Figure 103 for the 1st _{of July.}

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6.2.2.4. Thermal characterization of the buildings of the district

Finally, in order to analyse the thermal performance of the district a building model was developed for each of the 25 dwellings and the school by using HULCGIS, the tool developed in the present Thesis and explained in detail in Chapter 2. Once the simplified representations of the buildings were available, 3 main typologies were identified. The visits made to the district allowed to collect the necessary data of their real construction, geometry, occupancy and internal gains, which were then used for modelling them in detail (see Figure 104). Then, simulations were performed using the real climatic data during the monitoring period, allowing us to obtain the full free-floating temperatures.

Figure 104: HULC detailed models of dwelling 1 (left), dwelling 2 (center) and the school (right).

Once the building representations were available, the model explained in Chapter 4 for obtaining the energy baselines was applied.Due to the lack of empirical data that would allow us to obtain the real primary and secondary baselines, in this case they were obtained numerically, considering short time-steps of 15 minutes. After completing the process, the full free-floating temperatures as well as the secondary baselines for each building were available, which were essential to implement the precooling strategies proposed in the present work.